The Research of Intrusion Detection Based on Mixed Clustering Algorithm

Abstract

Nowadays, network security problems are increasing prominent, and how to find intrusion activities quickly and efficiently has become important to the security of system and network resource. we use the feature extraction and feature selection method of rough set and pattern recognition in the feature selection of network intrusion detection and introducing clustering method and genetic algorithm for network intrusion detection. First, we use the feature extraction which based on rough sets theory for the experimental data set. we use a mixed data dissimilarity algorithm and combining it with k-medoids algorithm. Makes the clustering algorithm can deal with a mixed data set which include continuous and discrete data. Last, traditional k-medoids clustering algorithm is difficult to determine the number of existing clustering, sensitive to initial value and easy to fall into local optimal solution. So we present an unsupervised clustering algorithm which combing with genetic algorithm and k-medoids clustering algorithm. All of these methods are efficiently to solve the defects of traditional k-medoids algorithm. And the algorithm can distinguish new attack from already existed attack.